{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # This mounts your Google Drive to the Colab VM.\n",
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')\n",
    "\n",
    "# # TODO: Enter the foldername in your Drive where you have saved the unzipped\n",
    "# # assignment folder, e.g. 'cs231n/assignments/assignment1/'\n",
    "# FOLDERNAME = None\n",
    "# assert FOLDERNAME is not None, \"[!] Enter the foldername.\"\n",
    "\n",
    "# # Now that we've mounted your Drive, this ensures that\n",
    "# # the Python interpreter of the Colab VM can load\n",
    "# # python files from within it.\n",
    "# import sys\n",
    "# sys.path.append('/content/drive/My Drive/{}'.format(FOLDERNAME))\n",
    "\n",
    "# # This downloads the CIFAR-10 dataset to your Drive\n",
    "# # if it doesn't already exist.\n",
    "# %cd /content/drive/My\\ Drive/$FOLDERNAME/cs231n/datasets/\n",
    "# !bash get_datasets.sh\n",
    "# %cd /content/drive/My\\ Drive/$FOLDERNAME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Convolutional Networks\n",
    "\n",
    "So far we have worked with deep fully connected networks, using them to explore different optimization strategies and network architectures. Fully connected networks are a good testbed for experimentation because they are very computationally efficient, but in practice all state-of-the-art results use convolutional networks instead.\n",
    "\n",
    "First you will implement several layer types that are used in convolutional networks. You will then use these layers to train a convolutional network on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Setup cell.\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.cnn import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient\n",
    "from cs231n.layers import *\n",
    "from cs231n.fast_layers import *\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (49000, 3, 32, 32)\n",
      "y_train: (49000,)\n",
      "X_val: (1000, 3, 32, 32)\n",
      "y_val: (1000,)\n",
      "X_test: (1000, 3, 32, 32)\n",
      "y_test: (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR-10 data.\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\n",
    "    print(f\"{k}: {v.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolution: Naive Forward Pass\n",
    "The core of a convolutional network is the convolution operation. In the file `cs231n/layers.py`, implement the forward pass for the convolution layer in the function `conv_forward_naive`. \n",
    "\n",
    "You don't have to worry too much about efficiency at this point; just write the code in whatever way you find most clear.\n",
    "\n",
    "You can test your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_naive\n",
      "difference:  2.2121476575931688e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "w_shape = (3, 3, 4, 4)\n",
    "x = np.linspace(-0.1, 0.5, num=np.prod(x_shape)).reshape(x_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=np.prod(w_shape)).reshape(w_shape)\n",
    "b = np.linspace(-0.1, 0.2, num=3)\n",
    "\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "out, _ = conv_forward_naive(x, w, b, conv_param)\n",
    "correct_out = np.array([[[[-0.08759809, -0.10987781],\n",
    "                           [-0.18387192, -0.2109216 ]],\n",
    "                          [[ 0.21027089,  0.21661097],\n",
    "                           [ 0.22847626,  0.23004637]],\n",
    "                          [[ 0.50813986,  0.54309974],\n",
    "                           [ 0.64082444,  0.67101435]]],\n",
    "                         [[[-0.98053589, -1.03143541],\n",
    "                           [-1.19128892, -1.24695841]],\n",
    "                          [[ 0.69108355,  0.66880383],\n",
    "                           [ 0.59480972,  0.56776003]],\n",
    "                          [[ 2.36270298,  2.36904306],\n",
    "                           [ 2.38090835,  2.38247847]]]])\n",
    "\n",
    "# Compare your output to ours; difference should be around e-8\n",
    "print('Testing conv_forward_naive')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aside: Image Processing via Convolutions\n",
    "\n",
    "As fun way to both check your implementation and gain a better understanding of the type of operation that convolutional layers can perform, we will set up an input containing two images and manually set up filters that perform common image processing operations (grayscale conversion and edge detection). The convolution forward pass will apply these operations to each of the input images. We can then visualize the results as a sanity check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from imageio import imread\n",
    "from PIL import Image\n",
    "\n",
    "kitten = imread('cs231n/notebook_images/kitten.jpg')\n",
    "puppy = imread('cs231n/notebook_images/puppy.jpg')\n",
    "# kitten is wide, and puppy is already square\n",
    "d = kitten.shape[1] - kitten.shape[0]\n",
    "kitten_cropped = kitten[:, d//2:-d//2, :]\n",
    "\n",
    "img_size = 200   # Make this smaller if it runs too slow\n",
    "resized_puppy = np.array(Image.fromarray(puppy).resize((img_size, img_size)))\n",
    "resized_kitten = np.array(Image.fromarray(kitten_cropped).resize((img_size, img_size)))\n",
    "x = np.zeros((2, 3, img_size, img_size))\n",
    "x[0, :, :, :] = resized_puppy.transpose((2, 0, 1))\n",
    "x[1, :, :, :] = resized_kitten.transpose((2, 0, 1))\n",
    "\n",
    "# Set up a convolutional weights holding 2 filters, each 3x3\n",
    "w = np.zeros((2, 3, 3, 3))\n",
    "\n",
    "# The first filter converts the image to grayscale.\n",
    "# Set up the red, green, and blue channels of the filter.\n",
    "w[0, 0, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n",
    "w[0, 1, :, :] = [[0, 0, 0], [0, 0.6, 0], [0, 0, 0]]\n",
    "w[0, 2, :, :] = [[0, 0, 0], [0, 0.1, 0], [0, 0, 0]]\n",
    "\n",
    "# Second filter detects horizontal edges in the blue channel.\n",
    "w[1, 2, :, :] = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]\n",
    "\n",
    "# Vector of biases. We don't need any bias for the grayscale\n",
    "# filter, but for the edge detection filter we want to add 128\n",
    "# to each output so that nothing is negative.\n",
    "b = np.array([0, 128])\n",
    "\n",
    "# Compute the result of convolving each input in x with each filter in w,\n",
    "# offsetting by b, and storing the results in out.\n",
    "out, _ = conv_forward_naive(x, w, b, {'stride': 1, 'pad': 1})\n",
    "\n",
    "def imshow_no_ax(img, normalize=True):\n",
    "    \"\"\" Tiny helper to show images as uint8 and remove axis labels \"\"\"\n",
    "    if normalize:\n",
    "        img_max, img_min = np.max(img), np.min(img)\n",
    "        img = 255.0 * (img - img_min) / (img_max - img_min)\n",
    "    plt.imshow(img.astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "\n",
    "# Show the original images and the results of the conv operation\n",
    "plt.subplot(2, 3, 1)\n",
    "imshow_no_ax(puppy, normalize=False)\n",
    "plt.title('Original image')\n",
    "plt.subplot(2, 3, 2)\n",
    "imshow_no_ax(out[0, 0])\n",
    "plt.title('Grayscale')\n",
    "plt.subplot(2, 3, 3)\n",
    "imshow_no_ax(out[0, 1])\n",
    "plt.title('Edges')\n",
    "plt.subplot(2, 3, 4)\n",
    "imshow_no_ax(kitten_cropped, normalize=False)\n",
    "plt.subplot(2, 3, 5)\n",
    "imshow_no_ax(out[1, 0])\n",
    "plt.subplot(2, 3, 6)\n",
    "imshow_no_ax(out[1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolution: Naive Backward Pass\n",
    "Implement the backward pass for the convolution operation in the function `conv_backward_naive` in the file `cs231n/layers.py`. Again, you don't need to worry too much about computational efficiency.\n",
    "\n",
    "When you are done, run the following to check your backward pass with a numeric gradient check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_backward_naive function\n",
      "dx error:  2.744926210890769e-09\n",
      "dw error:  2.514577507623979e-10\n",
      "db error:  6.632932008276166e-11\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(4, 3, 5, 5)\n",
    "w = np.random.randn(2, 3, 3, 3)\n",
    "b = np.random.randn(2,)\n",
    "dout = np.random.randn(4, 2, 5, 5)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "out, cache = conv_forward_naive(x, w, b, conv_param)\n",
    "dx, dw, db = conv_backward_naive(dout, cache)\n",
    "\n",
    "# Your errors should be around e-8 or less.\n",
    "print('Testing conv_backward_naive function')\n",
    "print('dx error: ', rel_error(dx, dx_num))\n",
    "print('dw error: ', rel_error(dw, dw_num))\n",
    "print('db error: ', rel_error(db, db_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Max-Pooling: Naive Forward Pass\n",
    "Implement the forward pass for the max-pooling operation in the function `max_pool_forward_naive` in the file `cs231n/layers.py`. Again, don't worry too much about computational efficiency.\n",
    "\n",
    "Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_forward_naive function:\n",
      "difference:  4.1666665157267834e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape)\n",
    "pool_param = {'pool_width': 2, 'pool_height': 2, 'stride': 2}\n",
    "\n",
    "out, _ = max_pool_forward_naive(x, pool_param)\n",
    "\n",
    "correct_out = np.array([[[[-0.26315789, -0.24842105],\n",
    "                          [-0.20421053, -0.18947368]],\n",
    "                         [[-0.14526316, -0.13052632],\n",
    "                          [-0.08631579, -0.07157895]],\n",
    "                         [[-0.02736842, -0.01263158],\n",
    "                          [ 0.03157895,  0.04631579]]],\n",
    "                        [[[ 0.09052632,  0.10526316],\n",
    "                          [ 0.14947368,  0.16421053]],\n",
    "                         [[ 0.20842105,  0.22315789],\n",
    "                          [ 0.26736842,  0.28210526]],\n",
    "                         [[ 0.32631579,  0.34105263],\n",
    "                          [ 0.38526316,  0.4       ]]]])\n",
    "\n",
    "# Compare your output with ours. Difference should be on the order of e-8.\n",
    "print('Testing max_pool_forward_naive function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Max-Pooling: Naive Backward\n",
    "Implement the backward pass for the max-pooling operation in the function `max_pool_backward_naive` in the file `cs231n/layers.py`. You don't need to worry about computational efficiency.\n",
    "\n",
    "Check your implementation with numeric gradient checking by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_backward_naive function:\n",
      "dx error:  3.27562514223145e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(3, 2, 8, 8)\n",
    "dout = np.random.randn(3, 2, 4, 4)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)\n",
    "\n",
    "out, cache = max_pool_forward_naive(x, pool_param)\n",
    "dx = max_pool_backward_naive(dout, cache)\n",
    "\n",
    "# Your error should be on the order of e-12\n",
    "print('Testing max_pool_backward_naive function:')\n",
    "print('dx error: ', rel_error(dx, dx_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fast Layers\n",
    "\n",
    "Making convolution and pooling layers fast can be challenging. To spare you the pain, we've provided fast implementations of the forward and backward passes for convolution and pooling layers in the file `cs231n/fast_layers.py`.\n",
    "\n",
    "### Execute the below cell, save the notebook, and restart the runtime\n",
    "The fast convolution implementation depends on a Cython extension; to compile it, run the cell below. Next, save the Colab notebook (`File > Save`) and **restart the runtime** (`Runtime > Restart runtime`). You can then re-execute the preceeding cells from top to bottom and skip the cell below as you only need to run it once for the compilation step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (4072626816.py, line 7)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"/tmp/ipykernel_16719/4072626816.py\"\u001b[0;36m, line \u001b[0;32m7\u001b[0m\n\u001b[0;31m    python setup.py build_ext --inplace\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# Remember to restart the runtime after executing this cell!\n",
    "# %cd /content/drive/My\\ Drive/$FOLDERNAME/cs231n/\n",
    "# !python setup.py build_ext --inplace\n",
    "# %cd /content/drive/My\\ Drive/$FOLDERNAME/\n",
    "\n",
    "# %cd /home/wxz/codes/cs231/assignment2/cs231n\n",
    "# python setup.py build_ext --inplace\n",
    "# %cd /home/wxz/codes/cs231/assignment2/\n",
    "\n",
    "\n",
    "\"\"\"这里直接用命令台进入 ./assignment2/cs231n 编译cypthon\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The API for the fast versions of the convolution and pooling layers is exactly the same as the naive versions that you implemented above: the forward pass receives data, weights, and parameters and produces outputs and a cache object; the backward pass recieves upstream derivatives and the cache object and produces gradients with respect to the data and weights.\n",
    "\n",
    "**Note:** The fast implementation for pooling will only perform optimally if the pooling regions are non-overlapping and tile the input. If these conditions are not met then the fast pooling implementation will not be much faster than the naive implementation.\n",
    "\n",
    "You can compare the performance of the naive and fast versions of these layers by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_fast:\n",
      "Naive: 0.046710s\n",
      "Fast: 0.002816s\n",
      "Speedup: 16.584779x\n",
      "Difference:  0.0\n",
      "\n",
      "Testing conv_backward_fast:\n",
      "Naive: 4.008810s\n",
      "Fast: 0.003027s\n",
      "Speedup: 1324.367202x\n",
      "dx difference:  1.949764775345631e-11\n",
      "dw difference:  4.0317310270899824e-13\n",
      "db difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "# Rel errors should be around e-9 or less.\n",
    "from cs231n.fast_layers import conv_forward_fast, conv_backward_fast\n",
    "from time import time\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(100, 3, 31, 31)\n",
    "w = np.random.randn(25, 3, 3, 3)\n",
    "b = np.random.randn(25,)\n",
    "dout = np.random.randn(100, 25, 16, 16)\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = conv_forward_naive(x, w, b, conv_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = conv_forward_fast(x, w, b, conv_param)\n",
    "t2 = time()\n",
    "\n",
    "print('Testing conv_forward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('Fast: %fs' % (t2 - t1))\n",
    "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('Difference: ', rel_error(out_naive, out_fast))\n",
    "\n",
    "t0 = time()\n",
    "dx_naive, dw_naive, db_naive = conv_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast, dw_fast, db_fast = conv_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print('\\nTesting conv_backward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('Fast: %fs' % (t2 - t1))\n",
    "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('dx difference: ', rel_error(dx_naive, dx_fast))\n",
    "print('dw difference: ', rel_error(dw_naive, dw_fast))\n",
    "print('db difference: ', rel_error(db_naive, db_fast))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing pool_forward_fast:\n",
      "Naive: 0.228254s\n",
      "fast: 0.002980s\n",
      "speedup: 76.607586x\n",
      "difference:  0.0\n",
      "\n",
      "Testing pool_backward_fast:\n",
      "Naive: 0.361652s\n",
      "fast: 0.006079s\n",
      "speedup: 59.494705x\n",
      "dx difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "# Relative errors should be close to 0.0.\n",
    "from cs231n.fast_layers import max_pool_forward_fast, max_pool_backward_fast\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(100, 3, 32, 32)\n",
    "dout = np.random.randn(100, 3, 16, 16)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = max_pool_forward_naive(x, pool_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = max_pool_forward_fast(x, pool_param)\n",
    "t2 = time()\n",
    "\n",
    "print('Testing pool_forward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('fast: %fs' % (t2 - t1))\n",
    "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('difference: ', rel_error(out_naive, out_fast))\n",
    "\n",
    "t0 = time()\n",
    "dx_naive = max_pool_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast = max_pool_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print('\\nTesting pool_backward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('fast: %fs' % (t2 - t1))\n",
    "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('dx difference: ', rel_error(dx_naive, dx_fast))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional \"Sandwich\" Layers\n",
    "In the previous assignment, we introduced the concept of \"sandwich\" layers that combine multiple operations into commonly used patterns. In the file `cs231n/layer_utils.py` you will find sandwich layers that implement a few commonly used patterns for convolutional networks. Run the cells below to sanity check their usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu_pool\n",
      "dx error:  6.514336569263308e-09\n",
      "dw error:  1.490843753539445e-08\n",
      "db error:  2.037390356217257e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_pool_forward, conv_relu_pool_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 16, 16)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "out, cache = conv_relu_pool_forward(x, w, b, conv_param, pool_param)\n",
    "dx, dw, db = conv_relu_pool_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], b, dout)\n",
    "\n",
    "# Relative errors should be around e-8 or less\n",
    "print('Testing conv_relu_pool')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu:\n",
      "dx error:  3.5600610115232832e-09\n",
      "dw error:  2.2497700915729298e-10\n",
      "db error:  1.3087619975802167e-10\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_forward, conv_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 8, 8)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "out, cache = conv_relu_forward(x, w, b, conv_param)\n",
    "dx, dw, db = conv_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_forward(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_forward(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_forward(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "# Relative errors should be around e-8 or less\n",
    "print('Testing conv_relu:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Three-Layer Convolutional Network\n",
    "Now that you have implemented all the necessary layers, we can put them together into a simple convolutional network.\n",
    "\n",
    "Open the file `cs231n/classifiers/cnn.py` and complete the implementation of the `ThreeLayerConvNet` class. Remember you can use the fast/sandwich layers (already imported for you) in your implementation. Run the following cells to help you debug:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sanity Check Loss\n",
    "After you build a new network, one of the first things you should do is sanity check the loss. When we use the softmax loss, we expect the loss for random weights (and no regularization) to be about `log(C)` for `C` classes. When we add regularization the loss should go up slightly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial loss (no regularization):  2.3025844271726545\n",
      "Initial loss (with regularization):  2.5090888615198073\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet()\n",
    "\n",
    "N = 50\n",
    "X = np.random.randn(N, 3, 32, 32)\n",
    "y = np.random.randint(10, size=N)\n",
    "\n",
    "loss, grads = model.loss(X, y)\n",
    "print('Initial loss (no regularization): ', loss)\n",
    "\n",
    "model.reg = 0.5\n",
    "loss, grads = model.loss(X, y)\n",
    "print('Initial loss (with regularization): ', loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Check\n",
    "After the loss looks reasonable, use numeric gradient checking to make sure that your backward pass is correct. When you use numeric gradient checking you should use a small amount of artifical data and a small number of neurons at each layer. Note: correct implementations may still have relative errors up to the order of e-2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 max relative error: 3.053965e-04\n",
      "W2 max relative error: 1.822723e-02\n",
      "W3 max relative error: 3.422399e-04\n",
      "b1 max relative error: 3.397321e-06\n",
      "b2 max relative error: 2.517459e-03\n",
      "b3 max relative error: 9.711800e-10\n"
     ]
    }
   ],
   "source": [
    "num_inputs = 2\n",
    "input_dim = (3, 16, 16)\n",
    "reg = 0.0\n",
    "num_classes = 10\n",
    "np.random.seed(231)\n",
    "X = np.random.randn(num_inputs, *input_dim)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "model = ThreeLayerConvNet(\n",
    "    num_filters=3,\n",
    "    filter_size=3,\n",
    "    input_dim=input_dim,\n",
    "    hidden_dim=7,\n",
    "    dtype=np.float64\n",
    ")\n",
    "loss, grads = model.loss(X, y)\n",
    "# Errors should be small, but correct implementations may have\n",
    "# relative errors up to the order of e-2\n",
    "for param_name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, model.params[param_name], verbose=False, h=1e-6)\n",
    "    e = rel_error(param_grad_num, grads[param_name])\n",
    "    print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overfit Small Data\n",
    "A nice trick is to train your model with just a few training samples. You should be able to overfit small datasets, which will result in very high training accuracy and comparatively low validation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 30) loss: 2.414060\n",
      "(Epoch 0 / 15) train acc: 0.130000; val_acc: 0.077000\n",
      "(Iteration 2 / 30) loss: 2.425621\n",
      "(Epoch 1 / 15) train acc: 0.150000; val_acc: 0.081000\n",
      "(Iteration 3 / 30) loss: 2.303886\n",
      "(Iteration 4 / 30) loss: 2.236292\n",
      "(Epoch 2 / 15) train acc: 0.210000; val_acc: 0.125000\n",
      "(Iteration 5 / 30) loss: 2.043194\n",
      "(Iteration 6 / 30) loss: 2.086255\n",
      "(Epoch 3 / 15) train acc: 0.270000; val_acc: 0.136000\n",
      "(Iteration 7 / 30) loss: 1.894367\n",
      "(Iteration 8 / 30) loss: 1.919015\n",
      "(Epoch 4 / 15) train acc: 0.460000; val_acc: 0.150000\n",
      "(Iteration 9 / 30) loss: 1.419001\n",
      "(Iteration 10 / 30) loss: 1.761951\n",
      "(Epoch 5 / 15) train acc: 0.600000; val_acc: 0.181000\n",
      "(Iteration 11 / 30) loss: 1.352713\n",
      "(Iteration 12 / 30) loss: 1.275495\n",
      "(Epoch 6 / 15) train acc: 0.740000; val_acc: 0.218000\n",
      "(Iteration 13 / 30) loss: 1.236787\n",
      "(Iteration 14 / 30) loss: 1.137474\n",
      "(Epoch 7 / 15) train acc: 0.800000; val_acc: 0.208000\n",
      "(Iteration 15 / 30) loss: 0.688182\n",
      "(Iteration 16 / 30) loss: 0.658294\n",
      "(Epoch 8 / 15) train acc: 0.820000; val_acc: 0.205000\n",
      "(Iteration 17 / 30) loss: 0.860531\n",
      "(Iteration 18 / 30) loss: 0.421917\n",
      "(Epoch 9 / 15) train acc: 0.900000; val_acc: 0.190000\n",
      "(Iteration 19 / 30) loss: 0.291448\n",
      "(Iteration 20 / 30) loss: 0.397220\n",
      "(Epoch 10 / 15) train acc: 0.920000; val_acc: 0.179000\n",
      "(Iteration 21 / 30) loss: 0.296664\n",
      "(Iteration 22 / 30) loss: 0.142035\n",
      "(Epoch 11 / 15) train acc: 0.940000; val_acc: 0.198000\n",
      "(Iteration 23 / 30) loss: 0.240883\n",
      "(Iteration 24 / 30) loss: 0.235767\n",
      "(Epoch 12 / 15) train acc: 0.990000; val_acc: 0.217000\n",
      "(Iteration 25 / 30) loss: 0.068360\n",
      "(Iteration 26 / 30) loss: 0.057110\n",
      "(Epoch 13 / 15) train acc: 1.000000; val_acc: 0.224000\n",
      "(Iteration 27 / 30) loss: 0.035496\n",
      "(Iteration 28 / 30) loss: 0.039566\n",
      "(Epoch 14 / 15) train acc: 0.980000; val_acc: 0.215000\n",
      "(Iteration 29 / 30) loss: 0.104520\n",
      "(Iteration 30 / 30) loss: 0.061727\n",
      "(Epoch 15 / 15) train acc: 1.000000; val_acc: 0.232000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "\n",
    "num_train = 100\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "model = ThreeLayerConvNet(weight_scale=1e-2)\n",
    "\n",
    "solver = Solver(\n",
    "    model,\n",
    "    small_data,\n",
    "    num_epochs=15,\n",
    "    batch_size=50,\n",
    "    update_rule='adam',\n",
    "    optim_config={'learning_rate': 1e-3,},\n",
    "    verbose=True,\n",
    "    print_every=1\n",
    ")\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "small_data_train_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Small data training accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "# Print final training accuracy.\n",
    "print(\n",
    "    \"Small data training accuracy:\",\n",
    "    solver.check_accuracy(small_data['X_train'], small_data['y_train'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "small_data_validation_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Small data validation accuracy: 0.232\n"
     ]
    }
   ],
   "source": [
    "# Print final validation accuracy.\n",
    "print(\n",
    "    \"Small data validation accuracy:\",\n",
    "    solver.check_accuracy(small_data['X_val'], small_data['y_val'])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the loss, training accuracy, and validation accuracy should show clear overfitting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o')\n",
    "plt.plot(solver.val_acc_history, '-o')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Network\n",
    "By training the three-layer convolutional network for one epoch, you should achieve greater than 40% accuracy on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 980) loss: 2.304636\n",
      "(Epoch 0 / 1) train acc: 0.129000; val_acc: 0.125000\n",
      "(Iteration 21 / 980) loss: 2.180788\n",
      "(Iteration 41 / 980) loss: 2.095185\n",
      "(Iteration 61 / 980) loss: 2.169457\n",
      "(Iteration 81 / 980) loss: 1.706804\n",
      "(Iteration 101 / 980) loss: 1.772216\n",
      "(Iteration 121 / 980) loss: 1.995572\n",
      "(Iteration 141 / 980) loss: 1.770316\n",
      "(Iteration 161 / 980) loss: 1.655821\n",
      "(Iteration 181 / 980) loss: 1.688137\n",
      "(Iteration 201 / 980) loss: 1.791303\n",
      "(Iteration 221 / 980) loss: 1.731009\n",
      "(Iteration 241 / 980) loss: 1.708217\n",
      "(Iteration 261 / 980) loss: 1.760324\n",
      "(Iteration 281 / 980) loss: 1.885783\n",
      "(Iteration 301 / 980) loss: 1.677713\n",
      "(Iteration 321 / 980) loss: 1.743382\n",
      "(Iteration 341 / 980) loss: 1.464815\n",
      "(Iteration 361 / 980) loss: 1.841351\n",
      "(Iteration 381 / 980) loss: 1.847649\n",
      "(Iteration 401 / 980) loss: 2.084611\n",
      "(Iteration 421 / 980) loss: 1.775527\n",
      "(Iteration 441 / 980) loss: 1.636713\n",
      "(Iteration 461 / 980) loss: 1.988669\n",
      "(Iteration 481 / 980) loss: 1.532413\n",
      "(Iteration 501 / 980) loss: 1.521093\n",
      "(Iteration 521 / 980) loss: 1.785183\n",
      "(Iteration 541 / 980) loss: 1.817418\n",
      "(Iteration 561 / 980) loss: 1.607775\n",
      "(Iteration 581 / 980) loss: 1.686789\n",
      "(Iteration 601 / 980) loss: 1.401188\n",
      "(Iteration 621 / 980) loss: 1.486707\n",
      "(Iteration 641 / 980) loss: 1.766979\n",
      "(Iteration 661 / 980) loss: 1.745248\n",
      "(Iteration 681 / 980) loss: 1.795278\n",
      "(Iteration 701 / 980) loss: 1.531627\n",
      "(Iteration 721 / 980) loss: 2.010237\n",
      "(Iteration 741 / 980) loss: 1.737461\n",
      "(Iteration 761 / 980) loss: 1.551681\n",
      "(Iteration 781 / 980) loss: 1.595397\n",
      "(Iteration 801 / 980) loss: 1.390202\n",
      "(Iteration 821 / 980) loss: 1.776279\n",
      "(Iteration 841 / 980) loss: 1.530124\n",
      "(Iteration 861 / 980) loss: 1.713471\n",
      "(Iteration 881 / 980) loss: 1.426927\n",
      "(Iteration 901 / 980) loss: 1.276658\n",
      "(Iteration 921 / 980) loss: 1.845595\n",
      "(Iteration 941 / 980) loss: 1.412101\n",
      "(Iteration 961 / 980) loss: 1.790985\n",
      "(Epoch 1 / 1) train acc: 0.459000; val_acc: 0.480000\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001)\n",
    "\n",
    "solver = Solver(\n",
    "    model,\n",
    "    data,\n",
    "    num_epochs=1,\n",
    "    batch_size=50,\n",
    "    update_rule='adam',\n",
    "    optim_config={'learning_rate': 1e-3,},\n",
    "    verbose=True,\n",
    "    print_every=20\n",
    ")\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "full_data_train_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full data training accuracy: 0.4770204081632653\n"
     ]
    }
   ],
   "source": [
    "# Print final training accuracy.\n",
    "print(\n",
    "    \"Full data training accuracy:\",\n",
    "    solver.check_accuracy(data['X_train'], data['y_train'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "full_data_validation_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full data validation accuracy: 0.48\n"
     ]
    }
   ],
   "source": [
    "# Print final validation accuracy.\n",
    "print(\n",
    "    \"Full data validation accuracy:\",\n",
    "    solver.check_accuracy(data['X_val'], data['y_val'])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Filters\n",
    "You can visualize the first-layer convolutional filters from the trained network by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dnIJ38ivH8XfIcur35YP//rtw7V+8jOfDrDQ++2p9+iLMfSk9N1dN47XZDdzsueW2D8D8sX/1pMo+0/0oXJsM4l0231zQs1ciIh5HGebuWlZlk1E8Gzfsa4V5M7ziISLjWHiIyDgWHiIyjoWHiIxrenN55V18hEZH8WmYr5d/qbKHqng7oFIJbxxVap2F+Zl1fGMLufVjD8J8+ofPwfzFV5+F+VT5LpX9s8EuuHabBx8FtD0wAnM7Fac+hmQxjzeOmp8twdxZwe+VVffAvGtQ36CvNfCNWzvHT16GedWhb6SupPBmWllfGObRAL55Wc6uw3xwC75JDR8jj//fOzm8C+alGn7uibjeDGvpKm7CeIp4ozb/XQdgjtRjN8G8Jn68PohvIhctfNROKquPfHLk8MZzKyX8eprhFQ8RGcfCQ0TGsfAQkXEsPERkHAsPERnXtKvV/iF9XI2IiPN5/Sf2IiIeT6fKTvwe3kvs5gY+KmPAj8cDTte2wBzZsf8OmM+uvQnz1Z/gP20vvas7FQcmcTfB17MV5vdejw+6t9Pq0Z2adm8Erk055mHu9uEuw9CWQZjv7NSdtMt13MGwM2PTqZqvp1W2sIr/fH82j49NivXjr6mrjPPhKO6OIaGQ/s6KiPSM4u9hYg13NRMFfRTSxhruDBZraZyv444u4uvCz7vqwuMYkSg+ImjRgTugvjn9nat4zuGfmdffn38Kr3iIyDgWHiIyjoWHiIxj4SEi41h4iMi4pl2tUvizMF+axHNJawO6m/BBN95QKRnEsz3Pe/H8TSCz+RoZ9uOuxh034M2T6incHXru0M9UtuPkP8C1D9+iuxoiIm0Dm+/GiYjc2N+usm278ezRchrPXlXncdeoxR2FedipH+fOXcNw7XmJwzzyWx+DeUevnsm7rkvPhomIJLz4M/bW8AZmuVXcNcrPg/mjb74B17rquKvjqeFN1jy+GswDQd1JbFj4cyit4vmobPI8zJH5oTGYr4SjMHc4+2Ae9OL1vj168zUrcRauLW+kYd4Mr3iIyDgWHiIyjoWHiIxj4SEi41h4iMi4pl2tyMjdMB9dxTNPn3DouaxEC57jeGvlJzBvSX0K5i/Y3JVHfjI1A/Mn79sO87wD71h47ryenTk8j+dp9gzhLsiWCN4Rzs50XM92zazin7lvJ35PekfxESeXjuLHOXz4dZU1nHhWyc47dfzetoSGVDYUwzNjgW48k5ZZwZ2n1DqeHTq5vghzJBTF75XPg7uxPsEd00infu6tXW1wbb6Iu8Lruaa/jv+XxNnjMM8FJ2A+045n71ztAzDPV9Iqa3PjHS89oo/C+afwioeIjGPhISLjWHiIyDgWHiIyjoWHiIxreht9/6AP5q5ZPU8kIjK7Xe/kF8/ZzKXkJmF+dg/udsXP7oM58o8X8E5293wIzzzdM3krfi4fnlbZlUs6ExE5dukCzPOR62Bup35Rdw5SgncUvFzIwdzvS8PcVcefZwPt+pjFHRk78eM/hfnF8/r1NFJ43slZxedn5R0JmMequMvisOlIIctreDbwcu0SXj+/AHNnWXcjuzvwrpSFJfw7kbY5bwtpW/45zAMjJ2GeWMa7hrrreA7OmayqzOtJ459Z1Gt/5TGbnFc8RHQNsPAQkXEsPERkHAsPERnX9OZy/4EbYX7s4imYV52nVfaPg/iG2doCPvbFfxSPLyzcjMc0kNkNvYmRiMilE/gm2Mg+fFTIvgP7VTYXxxthHVmZg3lyHv9pu507Jx5Qmb8F34x99/QZmF84h0cJxsf2wvzBG25TWdWFb/SekLdgfqcH37geBOMBy1U8MpBK4pvIVghveDa2FR+/NBodV9nX5Hm4NhrAYxqFDL5xXyrg8Y1It264jFb18xARWUuuwjyNTwiC7nYdhrlVxzeRBxq4KVKzRmBe8ugRi3CuDNfW6/hmeTO84iEi41h4iMg4Fh4iMo6Fh4iMY+EhIuMclqU37/o//+gAO3sREW2CZVkOu3/jFQ8RGcfCQ0TGsfAQkXEsPERkHAsPERnXdFbrpZ/hptZ/+PQzMM+c0jNSrU5c2x69Dx86P9mBZ1BioRWY3/b1r6hs/Z8/AtdeaO+A+VICP8cImD9q3ZiHawe36Tk1EZG+ON5QyvFXMJbDj+tNrKxSBa4tt+MjaIodeM4qGMZzOd66nqcr+brg2oP/7rsw99z9MsyrXv1c3HjkR9wePJNVD+IjggI2n6croOepUn+Jj9+Ze/5mmEeP4WNfzqxfD/P0zC0q++lFPNe1vhaC+doGnhl8RfaozPrUx+HaCw08A/l6FM+1ZRfwZmqyrAfH0uvH8FoX3kytGV7xEJFxLDxEZBwLDxEZx8JDRMax8BCRcU27Wq++8U2YX5x7BeYfuO2TKtvaNwzXHuzGd/wlgzsbF2QKr0cPIfholoAbH+WRiOHjekrrehe2FS9+bF8ed00WU/g4FJEiTMfvu1NlwRjuUnljEzAvuXAXKNnAu+0lcrob6cCb5NmatOmCBQO6M1iq4B0VLTc+lsby4s/NXcXdoapXPw7e81CkawHvkOmbxf9PXizj5+jJhlWWK+Gumz+Gn/e4hXe3fAX8qrx2YRGuvVTHn/1sMArz8soSzAt+3XpsWcE7e1Yq+HvVDK94iMg4Fh4iMo6Fh4iMY+EhIuOa3lw+OvUazEM34f19bt06pLJto/qmm4hIsf4Ozmv4qIyo23ZPISXy6/jP4J0ZfFNvxyy+eZsa139O7vDi8YUeL74x2NjZDXP53t/C+FKbfu4pm/2UMnF8o/tyHB/jU83i517Lg6+B6739P+m+he/BfNSnn2MqhRsLtQq+cetqxZ9b2YXXOyJRlZ2EK0VW/Hh0Z3l/H8wXLuExlRaPvmHu7sBNi2QB3yxvuRk3KOT7OjqcxE2YtRB+T5ZsJiNSrXg0pKWux6UGW/HxQw2/zYM3wSseIjKOhYeIjGPhISLjWHiIyDgWHiIyrmlXK58NwPyebbhrdPN+fWD8YhmPBlRsfnQ4gjcVquc2v9nQ3ywNwzy2sgbzTB53CBLdMZXtiuOuxrEQfn6uyK0wF8FdrTcu62wuj7tUC+U0zDequGsStPn/jNenP+c2P35P7PzbATyU0O3QG1NVSvjP/XMR/DP9fjwGsOINwjzj1l2zL8CVIuVp3AWqWb0wd27gTtU5l+6OrW3g0ZCIzeZ4ktDjJXZO1QZhnnXj55ct4o3A4n783XJlQEdXsnBtfeO9fVdEeMVDRNcACw8RGcfCQ0TGsfAQkXEsPERkXNOu1rZufLf6A3vxjFCqqjsY1fmzcG3Dwh2mRN2mC9aF50SQUApvWDS9Bc9kFZfx2xBK6Tmz+g78GJEq/pkOD56PsrNS1++tZbMRWK2KN5pyt+I8W8JHn3R59CxUsqo3QWtmOj0Nc0v04zjL+LMsJdMwz4ZtXk8Ed4GSQdzVQ8ot+PvmmMKfW7Ubd40GV/XrDPU04NpiAv9e3dCPPx/kShJv4LUBNiQTEal24g5byo3ztoCe1VrO4u6iq25zXlETvOIhIuNYeIjIOBYeIjKOhYeIjGPhISLjmne1RvXslYiIawjPsWQv6A7W5bWrcG3ZWsZPKIjv1gcTm9+BULK4UxG5gh/DM4Lv7Acs3X3JFnFHIr+Ma/jWAJ53s5P06fc2J51wrd8dhXnW0jNmIiL1Cj7K5WpGz+u4NjbfGRIRmerGnbeyG7x+B37shg93gSSKu1eWF3eB4h48f4Xkw/i7nOvDnduQzUE52Q69fqOOZ8kSdfw5zDbwLpZILY6fdyo0AnOXF3fj0lXcqaoG9e9EQHA9cDtxh7oZXvEQkXEsPERkHAsPERnHwkNExrHwEJFxTbta+4fxHe9ICHcfpgpvq6wwh2d4XHtw56HfUYB5d9vmu1rBzCGY923Hu7DlS7fA3KnHVSS/dAWunXDhs6I6agswt5Oc1t2EogvPTWVacXek5sCfTyOP81BZ///Hv2HTYbIxFcPnhxWDUZVZLvy9KtbxTFaLzZlqVQ9+/ZUA7kghQ1P4vW1r/Ajm+fBBmLuP6k7qqhe/J94c7tz62nDHDNkeTcO8c2MF5iu1CZiHG/g9dG3o19PiAb8QIhIq4Mdohlc8RGQcCw8RGcfCQ0TGsfAQkXEOy8I3jEREHA6H/T8SETVhWZZtR4hXPERkHAsPERnHwkNExrHwEJFxLDxEZFzTkYmnvo6PppnahccAHgtcVNlLXXiToMVDeNOj3UN4k6jja3hDre889mWVRe74j3Cts5iDeWcrfuz2bXoDrsmxHXBtm82mXBLFj/2VJ/fA/E8fv05liTz+0/tLRbwZlHv0PpiHPfi9vW54QGUTB3BD4p5bb4T5ya99EebZkB6BOdMyBteu5vERQc78OzDvncWjMQPVOZU98FW4VP7r574D80A3Piamry0C84pXj5hUHDabxiXwWNDKKh67+dwXfkNlT3z5Mbh2395hmC+t4SOFls7Ownx4dFRlATf+/pTy722zOxFe8RDRNcDCQ0TGsfAQkXEsPERkHAsPERnXtKv1zdi3YH5v6Xdh/vaMPujevQ13Km56E3eYTgzsgvlYj+6Y2XncZsOv6Ag+giW2bQjmI0N6Y6pwdwiunb6KOxWnT8/A3E5jcV1l8QL+/0Ojqwvmba14Q62O9j6Yu7bo92U2iT8fO406PvokVNFHvHi8+HMITeljdkREOrfi7l3b8CTM62XUeToN1ybc+D1ssfCROi0evL7eojc3q+Xx67E7lsfvxZt4IesV/PnE62WYh4L4O+TpxB2pRk136RJp3IkOeXpg3gyveIjIOBYeIjKOhYeIjGPhISLjWHiIyLimXa2dldthftqJZ2c+epPuar1zHM82nRnHd/w/uYQ3PXzeOQ5zpKeAZ8y2ZvDM04SvBPNGekZl8Sv4eSffPgfzhTk8C2NnPbWostVVnYmIFCP49eyM4Hmi1ip+7i637siUqvjYFzueXtxhWnfpo0+yTvx+l9vwsTSL/gsw9y/g7lC7c/PH20wndBdRRKQwh+emXk0ch3mjod9bq6F/H0REumN6Nk5EJOja/JFCQ4M2v7q5VRinKri73BvCn0Uoop+L24lnz6q1NH4uTfCKh4iMY+EhIuNYeIjIOBYeIjKOhYeIjGva1ZrcFYP5OefdMP+fo6+obMeZk3BtqTaPH6PvAZj3F/Guh8hdI7ir0dvhhXmkhu/sz56/orKlS3hepT6LO09dG1mY28k2dFevVsPPe6AFz16Fw3jOqM2BP8+wT3fH1jb0Ln7NvOPB81SrqZrKjtTx51OKTcP8JgfuDpUEvx6BHZzX4dLl+cswn7qEO2nLVy/B3CrqrlYoiHfsm9iKZwm39mx+5mmljLtU1x3AXeTlq/j3bept/HrGnPo7VKnj6xSrYfM5NMErHiIyjoWHiIxj4SEi41h4iMi4pjeXHWNTMB924xu9t1R/qjLfJB67mBp+C+b/cjED8xecZ2CO3H7TTph7yvjIlsU5vElU9ry+8Ziw+RP7ah7/if1A7L0d/ZHb0EfqVDqjcG135wjMg3l8UzPvw2MQ0XW9WVetjF+PnUoSv85W35IOF/XPExHpDuERiHIW34we68WjOyM5PQJiZ3IA3+jt8uEb9AVw5JGIiKNcVJnbh19nLIw/n84IbhbIz3R0zOam8BNPPgrzHTvxd+XNl34O86nassp27cI3rnPLPN6GiP4/wMJDRMax8BCRcSw8RGQcCw8RGde0q/Xtk7pLJSJybOzfwPyPXfpomoYXH3syE8ZjCi/ccx7m+762+S7LH33jhzBfvTgD82mbvOYEG2f5ca0OBKIwHwjbdCps9I8OqmwsgjdB87WPwtxbszkOZx3/2bzTpV9nW+ua3VOEWt74PsyzXj0GMOy3eR6BNMy3B/B4QPdsCudB3L1EfmsId1Fbb9sC87GBCZh7fPpYmWoV/3ot2mwytr6SgPmXQDY+PAbXXpnCncvsOv48z53C+X0PXq+yyesPwLXH8+9tvEaEVzxEdA2w8BCRcSw8RGQcCw8RGcfCQ0TGNe1qXV3+dZhvL7wK819s6BmZqRt/BNdOfBnP04w9jjdm+sJVPCOF/HJ+BeZrcb0plYhIoQXPArVv0/M6/e2tcG0shjdx6tths0nSaz+GsTu6VWWV8DBc62vFx9hkE7g7InNgbkpE2mJRlfWE3tv8zeIC7mz4u/TnvCWLN/aqduh5JxERK4A3U3NGcQcntRSGOdJYw3N69Sp+/TlHCOZhcPJL1amP9hERKc7iI5wq8/hoGuTeex+BeTKF5yhPHcWfzx333o0f/+BBlTU28PE2ySTOm+EVDxEZx8JDRMax8BCRcSw8RGQcCw8RGeewLHyHXUTE4XDY/yMRUROWZdkOzfGKh4iMY+EhIuNYeIjIOBYeIjKu6cjEf/vi38B8fJfe8EtE5PJV/WfZhy/o88dFRBbncR5w4jGA3okBmP/1n35aZW+9/q/h2norPoP8nBsfZRL26fVuS2+QJCKyo47HEdp78WjIYPTjMCd6P+AVDxEZx8JDRMax8BCRcSw8RGQcCw8RGde0q3Xhabzh19BD+LiRfbtvUdlEjz6uRUTk0CncYTo/gzf8is/izb2Qc2m80dTpJXyUx9UcPm6kWtMbhBWsY3At3gZMZDiKu3FE72e84iEi41h4iMg4Fh4iMo6Fh4iMY+EhIuOadrUuz+IjMQLP4/3BJhL6eJLrHrwLrn1g9w0w7+rD3asTb8/AHHnn5V/C/GKwDPPz2RTMV9z67akt447eDqsA89lAN8yJ3s94xUNExrHwEJFxLDxEZBwLDxEZx8JDRMY17Wp1R/HB9SfO44Pul4q6CxYN1ODagx/+EMx3j3fBfFd/L8yfelpnt33xT+DaUZujfA529MM8WAurrODBtbqwehzm7dUgzN9+Gr9+ovcDXvEQkXEsPERkHAsPERnHwkNExrHwEJFxTbta44/dDvOFF16B+ZH4tMqqz/0Ark35qjB/5CMfgfmd+za/k9/x3EWYLyWzMM8WkjD3DOi3p2sDz17155fxY3fiLh3R+xmveIjIOBYeIjKOhYeIjGPhISLjmt5c3vbw/TD3790O86OHX1PZO784D9ce/9ZzMP/781dgfv/tB2GOlPJ4M7FSvwfmy9U4zDcW9cZhl3IuuHbc1YB5OO2DOdH7Ga94iMg4Fh4iMo6Fh4iMY+EhIuNYeIjIuKZdreU+vIlVe9f1ML9ndFBlbSPH4NrXDx2B+asnzsH8wuwMzJE9E7iT1JnpgXlFcjDP+BwqK2cTcG3Ej4+9KWSuwpzo/YxXPERkHAsPERnHwkNExrHwEJFxLDxEZJzDsjnyhYjo/xVe8RCRcSw8RGQcCw8RGcfCQ0TGsfAQkXEsPERk3P8C4ZJ7570QUo0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "grid = visualize_grid(model.params['W1'].transpose(0, 2, 3, 1))\n",
    "plt.imshow(grid.astype('uint8'))\n",
    "plt.axis('off')\n",
    "plt.gcf().set_size_inches(5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatial Batch Normalization\n",
    "We already saw that batch normalization is a very useful technique for training deep fully connected networks. As proposed in the original paper (link in `BatchNormalization.ipynb`), batch normalization can also be used for convolutional networks, but we need to tweak it a bit; the modification will be called \"spatial batch normalization.\"\n",
    "\n",
    "Normally, batch-normalization accepts inputs of shape `(N, D)` and produces outputs of shape `(N, D)`, where we normalize across the minibatch dimension `N`. For data coming from convolutional layers, batch normalization needs to accept inputs of shape `(N, C, H, W)` and produce outputs of shape `(N, C, H, W)` where the `N` dimension gives the minibatch size and the `(H, W)` dimensions give the spatial size of the feature map.\n",
    "\n",
    "If the feature map was produced using convolutions, then we expect every feature channel's statistics e.g. mean, variance to be relatively consistent both between different images, and different locations within the same image -- after all, every feature channel is produced by the same convolutional filter! Therefore, spatial batch normalization computes a mean and variance for each of the `C` feature channels by computing statistics over the minibatch dimension `N` as well the spatial dimensions `H` and `W`.\n",
    "\n",
    "\n",
    "[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatial Batch Normalization: Forward Pass\n",
    "\n",
    "In the file `cs231n/layers.py`, implement the forward pass for spatial batch normalization in the function `spatial_batchnorm_forward`. Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before spatial batch normalization:\n",
      "  shape:  (2, 3, 4, 5)\n",
      "  means:  [9.33463814 8.90909116 9.11056338]\n",
      "  stds:  [3.61447857 3.19347686 3.5168142 ]\n",
      "After spatial batch normalization:\n",
      "  shape:  (2, 3, 4, 5)\n",
      "  means:  [ 6.18949336e-16  5.99520433e-16 -1.22124533e-16]\n",
      "  stds:  [0.99999962 0.99999951 0.9999996 ]\n",
      "After spatial batch normalization (nontrivial gamma, beta):\n",
      "  shape:  (2, 3, 4, 5)\n",
      "  means:  [6. 7. 8.]\n",
      "  stds:  [2.99999885 3.99999804 4.99999798]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "\n",
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after spatial batch normalization.\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 4 * np.random.randn(N, C, H, W) + 10\n",
    "\n",
    "print('Before spatial batch normalization:')\n",
    "print('  shape: ', x.shape)\n",
    "print('  means: ', x.mean(axis=(0, 2, 3)))\n",
    "print('  stds: ', x.std(axis=(0, 2, 3)))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "gamma, beta = np.ones(C), np.zeros(C)\n",
    "bn_param = {'mode': 'train'}\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print('After spatial batch normalization:')\n",
    "print('  shape: ', out.shape)\n",
    "print('  means: ', out.mean(axis=(0, 2, 3)))\n",
    "print('  stds: ', out.std(axis=(0, 2, 3)))\n",
    "\n",
    "# Means should be close to beta and stds close to gamma\n",
    "gamma, beta = np.asarray([3, 4, 5]), np.asarray([6, 7, 8])\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print('After spatial batch normalization (nontrivial gamma, beta):')\n",
    "print('  shape: ', out.shape)\n",
    "print('  means: ', out.mean(axis=(0, 2, 3)))\n",
    "print('  stds: ', out.std(axis=(0, 2, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After spatial batch normalization (test-time):\n",
      "  means:  [-0.08034398  0.07562874  0.05716365  0.04378379]\n",
      "  stds:  [0.96718652 1.02997042 1.02887526 1.0058548 ]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "\n",
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "N, C, H, W = 10, 4, 11, 12\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(C)\n",
    "beta = np.zeros(C)\n",
    "for t in range(50):\n",
    "  x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "  spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "a_norm, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After spatial batch normalization (test-time):')\n",
    "print('  means: ', a_norm.mean(axis=(0, 2, 3)))\n",
    "print('  stds: ', a_norm.std(axis=(0, 2, 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatial Batch Normalization: Backward Pass\n",
    "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_batchnorm_backward`. Run the following to check your implementation using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  2.786648197756335e-07\n",
      "dgamma error:  7.0974817113608705e-12\n",
      "dbeta error:  3.275608725278405e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 5 * np.random.randn(N, C, H, W) + 12\n",
    "gamma = np.random.randn(C)\n",
    "beta = np.random.randn(C)\n",
    "dout = np.random.randn(N, C, H, W)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fb = lambda b: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "#You should expect errors of magnitudes between 1e-12~1e-06\n",
    "_, cache = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = spatial_batchnorm_backward(dout, cache)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatial Group Normalization\n",
    "In the previous notebook, we mentioned that Layer Normalization is an alternative normalization technique that mitigates the batch size limitations of Batch Normalization. However, as the authors of [2] observed, Layer Normalization does not perform as well as Batch Normalization when used with Convolutional Layers:\n",
    "\n",
    ">With fully connected layers, all the hidden units in a layer tend to make similar contributions to the final prediction, and re-centering and rescaling the summed inputs to a layer works well. However, the assumption of similar contributions is no longer true for convolutional neural networks. The large number of the hidden units whose\n",
    "receptive fields lie near the boundary of the image are rarely turned on and thus have very different\n",
    "statistics from the rest of the hidden units within the same layer.\n",
    "\n",
    "The authors of [3] propose an intermediary technique. In contrast to Layer Normalization, where you normalize over the entire feature per-datapoint, they suggest a consistent splitting of each per-datapoint feature into G groups and a per-group per-datapoint normalization instead. \n",
    "\n",
    "<p align=\"center\">\n",
    "<img src=\"https://raw.githubusercontent.com/cs231n/cs231n.github.io/master/assets/a2/normalization.png\">\n",
    "</p>\n",
    "<center>Visual comparison of the normalization techniques discussed so far (image edited from [3])</center>\n",
    "\n",
    "Even though an assumption of equal contribution is still being made within each group, the authors hypothesize that this is not as problematic, as innate grouping arises within features for visual recognition. One example they use to illustrate this is that many high-performance handcrafted features in traditional computer vision have terms that are explicitly grouped together. Take for example Histogram of Oriented Gradients [4] -- after computing histograms per spatially local block, each per-block histogram is normalized before being concatenated together to form the final feature vector.\n",
    "\n",
    "You will now implement Group Normalization.\n",
    "\n",
    "[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)\n",
    "\n",
    "\n",
    "[3] [Wu, Yuxin, and Kaiming He. \"Group Normalization.\" arXiv preprint arXiv:1803.08494 (2018).](https://arxiv.org/abs/1803.08494)\n",
    "\n",
    "\n",
    "[4] [N. Dalal and B. Triggs. Histograms of oriented gradients for\n",
    "human detection. In Computer Vision and Pattern Recognition\n",
    "(CVPR), 2005.](https://ieeexplore.ieee.org/abstract/document/1467360/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatial Group Normalization: Forward Pass\n",
    "\n",
    "In the file `cs231n/layers.py`, implement the forward pass for group normalization in the function `spatial_groupnorm_forward`. Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before spatial group normalization:\n",
      "  shape:  (2, 6, 4, 5)\n",
      "  means:  [9.72505327 8.51114185 8.9147544  9.43448077]\n",
      "  stds:  [3.67070958 3.09892597 4.27043622 3.97521327]\n",
      "After spatial group normalization:\n",
      "  shape:  (2, 6, 4, 5)\n",
      "  means:  [-2.14643118e-16  5.25505565e-16  2.65528340e-16 -3.38618023e-16]\n",
      "  stds:  [0.99999963 0.99999948 0.99999973 0.99999968]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "\n",
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after spatial batch normalization.\n",
    "N, C, H, W = 2, 6, 4, 5\n",
    "G = 2\n",
    "x = 4 * np.random.randn(N, C, H, W) + 10\n",
    "x_g = x.reshape((N*G,-1))\n",
    "print('Before spatial group normalization:')\n",
    "print('  shape: ', x.shape)\n",
    "print('  means: ', x_g.mean(axis=1))\n",
    "print('  stds: ', x_g.std(axis=1))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "gamma, beta = np.ones((1,C,1,1)), np.zeros((1,C,1,1))\n",
    "bn_param = {'mode': 'train'}\n",
    "\n",
    "out, _ = spatial_groupnorm_forward(x, gamma, beta, G, bn_param)\n",
    "out_g = out.reshape((N*G,-1))\n",
    "print('After spatial group normalization:')\n",
    "print('  shape: ', out.shape)\n",
    "print('  means: ', out_g.mean(axis=1))\n",
    "print('  stds: ', out_g.std(axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatial Group Normalization: Backward Pass\n",
    "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_groupnorm_backward`. Run the following to check your implementation using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  7.413109964655058e-08\n",
      "dgamma error:  9.468195772749234e-12\n",
      "dbeta error:  3.354494437653335e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, C, H, W = 2, 6, 4, 5\n",
    "G = 2\n",
    "x = 5 * np.random.randn(N, C, H, W) + 12\n",
    "gamma = np.random.randn(1,C,1,1)\n",
    "beta = np.random.randn(1,C,1,1)\n",
    "dout = np.random.randn(N, C, H, W)\n",
    "\n",
    "gn_param = {}\n",
    "fx = lambda x: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n",
    "fg = lambda a: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n",
    "fb = lambda b: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "_, cache = spatial_groupnorm_forward(x, gamma, beta, G, gn_param)\n",
    "dx, dgamma, dbeta = spatial_groupnorm_backward(dout, cache)\n",
    "\n",
    "# You should expect errors of magnitudes between 1e-12 and 1e-07. \n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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